Amazon Technologies, Inc.

Avik Sinha

Archiman Dutta

Deepak Kumar Nayak

Shoubhik Bhattacharya

Subhadeep Chakraborty

Rahul Gupta

Kevin Michael Small

Dhinesh Kumar Dhanasekaran

Mohammed Hidayath Ansari

Nathan Rose

At a machine learning service, a data structure generated during the training phase of a machine learning model, as well as an input records associated with a result of the model, are analyzed. A first informational data set pertaining to the result, which indicates an alternative result, is generated. The first informational data set is transmitted to a presentation device with a directive to display a visual representation of the data set. In response to an exploration request pertaining to the first informational data set, a second informational data set indicating one or more observations of a training data set used for the model is transmitted to the presentation device.

Network-accessible service for exploration of machine learning models and results

Methods, systems, and computer-readable media for taxonomy generation with automated analysis and auditing are disclosed. A primary classification is determined for a hierarchical taxonomy of items in a marketplace. The primary classification is selected from a plurality of terms describing items in the marketplace, and the primary classification is selected based at least in part on automated analysis of the terms. A plurality of secondary classifications are determined for the hierarchical taxonomy. The secondary classifications are selected from the terms describing the items in the marketplace, and the secondary classifications are selected based at least in part on automated analysis of the terms. The hierarchical taxonomy is modified based at least in part on feedback from a plurality of users. The feedback comprises one or more terms entered by one or more of the users to filter a set of items.

Taxonomy generation with statistical analysis and auditing

A technology for determining accuracy estimates for classifications used in an electronic catalog. In one example, classifications for product groupings included in an electronic catalog may be updated as a result of the classifications inaccurately representing products included in the product groupings. The electronic catalog of products may be grouped into a plurality of product groupings using classifications. Classifications of product groupings that inaccurately represent products included in the product grouping may be updated with suggested classifications. Update metrics for updates made to the grouping classifications may be collected and the update metrics may be used to calculate an accuracy estimate for the classifications used in the electronic catalog.

Classification accuracy estimation

Techniques are provided herein for utilizing a classification engine to improve a classification model. For example, a classification engine may derive a statistical model based at least in part on a synthetic data set. A misclassification may be determined based at least in part on an output of the statistical model. An audit question may be provided to an individual, the audit question being determined based at least in part on the determined misclassification. Response data related to the audit question may be received. The statistical model may be validated based at least in part on the response data.

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Seattle, WA, United States of America

Avik Sinha